NEJM AI

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NEJM AI, a new monthly journal from the publisher of @nejm.org, explores the cutting-edge applications of artificial intelligence and machine learning in clinical medicine. Online at ai.nejm.org.

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NEJM AI@ai· 15h

A new study evaluated the implementation of a machine learning–based clinical deterioration prediction model integrated with automated rapid response team notifications across 11 hospitals in a large regional health system. Full study results: https://nejm.ai/3TsbwbS

A sequence of illustrations depicting a patient's progression from walking independently to using a walker, and finally being assisted in a wheelchair. The background includes health chart graphics and age milestones. Color-coded symbols for prescription, medical care, and caution appear at different stages of the timeline.

NEJM AI@ai· 1d

The future of medical AI will require clinically grounded datasets, shared benchmarks, multimodal learning, and annotation systems that make expert knowledge easier to capture. Read the editorial by Todd Hollon, MD: https://nejm.ai/44Nz5hT

NEJM AI@ai· 2d

The first public benchmark and paired CT and MR angiography dataset for topology-aware circle of Willis segmentation demonstrates that AI models can delineate cerebrovascular anatomy across diverse imaging modalities and institutions. Learn more: https://nejm.ai/4fnSMTs

A figure showing the progression of TopCoW submissions and winning strategies. Panel A shows the performance of the five teams that participated in both years on the same 34 patients in each year’s test sets. The metrics shown are class-average Dice scores, variant-balanced accuracy of the anterior and posterior circle of Willis variant classifications, and average F1 score for detection of anterior communicating arteries, posterior communicating arteries, and third A2 arteries. Panel B shows key characteristics of the segmentation algorithms from the top six teams from both tracks in alphabetical team name order. Panel C shows two methodological breakthroughs for segmentation in 2023 that got picked up by many more teams in the following year.

NEJM AI@ai· 5d

Don’t build for today’s model. Build for tomorrow’s. On the NEJM AI Grand Rounds podcast, Brandon Rice explains why Weave treats AI as the engine — not the vehicle. Listen to latest episode: https://nejm.ai/ep44

NEJM AI@ai· 6d

A new editorial examines the limitations of current regulatory approaches for clinical AI and argues for continuous postdeployment oversight as part of an evolving regulatory framework. Full editorial: https://nejm.ai/4wffVgQ #ArtificialIntelligence #AIinMedicine

Figure 1 illustrates the current versus proposed regulatory pathways for clinical AI. Panel A: The current pathway, which relies on retrospective evidence and includes no systematic postmarket evaluation. Panel B: The proposed pathway, which adds multisite validation and continuous monitoring with a feedback loop for reevaluation when performance thresholds are not met.

NEJM AI@ai· 7d

A new Case Study maps ophthalmology AI research against workforce capacity and blindness burden, showing how concentrated AI development in high-income countries could worsen global health inequities. Learn more: https://nejm.ai/4aPRgqG #ArtificialIntelligence #AIinMedicine

Three density-weighted cartograms display global distributions. Panel A presents age-adjusted prevalence of blindness (%) in 2020; Panel B presents number of ophthalmologists per million population; and Panel C presents number of AI publications in ophthalmology from 2008 to 2025.

NEJM AI@ai· 9d

A new study evaluates Flourish, a mobile app using generative AI to deliver personalized, strengths-based well-being support to young adults through an AI well-being coach, in a 6-week randomized trial across three U.S. campuses. Learn more: https://nejm.ai/3TJ9iVy

Illustration of three individuals walking across large, stacked smartphone screens. A heading below reads: "AI for Proactive Mental Health: A Multi-Institutional, Longitudinal Randomized Controlled Trial."

NEJM AI@ai· 9d

Before a drug reaches patients, it has to clear a critical gate. In the latest episode of the NEJM AI Grand Rounds podcast, Brandon Rice of Weave explains why the Investigational New Drug (IND) process shapes everything that follows. Learn more: https://nejm.ai/ep44

NEJM AI@ai· 12d

A new Perspective examines the impact of AI on national health care expenditures, arguing that near-term cost reductions will depend heavily on policy and management choices rather than the technology alone. Learn more: https://nejm.ai/4f3PfcX #ArtificialIntelligence #AIinMedicine

NEJM AI@ai· 14d

Drug development has embraced cutting-edge science. The paperwork often hasn’t. In the latest episode of the NEJM AI Grand Rounds podcast, Brandon Rice of Weave explains why regulatory workflows are a major opportunity for AI. Full episode: https://nejm.ai/ep44

NEJM AI@ai· 14d

A new Perspective examines why pathology, despite being foundational to modern diagnostics, has seen limited clinical adoption of AI-enabled imaging tools. It outlines barriers and pathways for AI-assisted workflows and clinical trial applications. Learn more: https://nejm.ai/44xQhI1

NEJM AI@ai· 19d

Curiosity came first. Entrepreneurship came later. In the latest episode of the NEJM AI Grand Rounds podcast, Brandon Rice traces the path from building computers as a kid to building Weave. Listen to the full episode: https://nejm.ai/ep44

NEJM AI@ai· 20d

Perspective by Kushal T. Kadakia, MD, MSc, and Harlan M. Krumholz, MD, SM: Adoption of Artificial Intelligence–Based End Points — A New Era for Clinical Trials https://nejm.ai/4wkloTl

NEJM AI@ai· 21d

Original Article by O.L. Saldanha et al.: Privacy-Preserving Surgical Video Analysis with Swarm Learning — Results from a Multinational Appendectomy Cohort https://nejm.ai/44Ap0V9

This infographic illustrates a study on predicting patient outcomes from surgical video data using Swarm learning. It includes sections on data collection, preprocessing, and model development with flowcharts. Diagrams detail the process for non-perforated and perforated appendicitis alongside intraoperative images showing different grades. The figure highlights traditional and Swarm learning approaches across multiple centers.

NEJM AI@ai· 22d

Case Study by A.S. Liang et al.: SmartAlert — Implementing Machine Learning–Driven Clinical Decision Support for Inpatient Laboratory Utilization Reduction https://nejm.ai/4ezJxzb

Flowchart illustrating the CBC Utilization SmartAlert Development and Governance Process. It begins with lab utilization preparation in November 2022 and continues through stages including stakeholder meetings, committee reviews, and pilot implementations. Key milestones include a planned SEP SmartAlert enterprise go-live in September 2025. The chart features color-coded diamonds and rectangles representing different phases and actions, connected by arrows to indicate process flow.

NEJM AI@ai· 23d

An AI-derived and validated machine-learning system appeared to predict the risk of Hirschsprung disease with the use of genetic and demographic data from participants in China. Learn more in a correspondence published in NEJM: https://nej.md/4xVlzWE

This image contains a flowchart and line graphs illustrating use of the machine-learning system for the detection of Hirschsprung disease in the validation and external testing datasets. The flowchart in Panel A shows training and testing of machine-learning system for detection of Hirschsprung disease risk. Panel B presents the area under the ROC curves for Hirschsprung disease risk detection models.

NEJM AI@ai· 26d

A new Perspective examines the first European Medicines Agency and U.S. Food and Drug Administration endorsements of an AI-enabled clinical trial end point tool — AIM-NASH — and explores implications for evidence generation. Learn more: https://nejm.ai/4wkloTl

Table outlining potential applications of artificial intelligence for clinical trial endpoints. It includes focus areas such as histopathology, anatomical imaging, and physiological imaging. Descriptions and current examples are provided alongside AI-enabled future possibilities for each category.

NEJM AI@ai· 27d

A new study introduces a decentralized, privacy-preserving pipeline that combines weakly supervised deep learning with Swarm Learning to predict patient-level labels from laparoscopic appendectomy videos. Learn more about this approach: https://nejm.ai/44Ap0V9

A digital illustration showing a medical diagram of human organs overlaid with surgical tools. At the bottom, there's a text box containing the article title: "Privacy-Preserving Surgical Video Analysis with Swarm Learning — Results from a Multinational Appendectomy Cohort" by O.L. Saldanha and Others.

NEJM AI@ai· 28d

In the latest episode of the NEJM AI Grand Rounds podcast, Brandon Rice of Weave discusses the challenge of organizing scientific knowledge, communicating with regulators, and navigating processes that can span more than a decade. Listen now: https://nejm.ai/ep44

Promotional image for episode 44 of the NEJM AI Grand Rounds podcast featuring Weave’s Brandon Rice on rebuilding drug regulation with AI. Includes a photo of the guest.

NEJM AI@ai· 29d

SmartAlert — a machine learning–driven clinical decision support system targeting inpatient complete blood count utilization — reduced repetitive testing. Success depended on end-user engagement, implementation, and governance. Learn more: https://nejm.ai/4ezJxzb

Table depicting demographics and outcomes of CBC Utilization SmartAlert Pilot. It displays data for treatment and control groups, including number of encounters, alerts, ages, and race percentages. Rates for ICU admission and mortality are included.

NEJM AI@ai· Jul 10

Datasets, Benchmarks, and Protocols by M. Baharoon et al.: ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports https://nejm.ai/4giw8N6 #ArtificialIntelligence #AIinMedicine

This image is a diagram titled "Potential Downstream Applications of ReXGroundingCT." It is divided into two main sections: Training and Inference Tasks. In the Training section, a stack of cylindrical objects representing the ReXGroundingCT is connected to a neural network diagram. In the Inference Tasks section, two processes are shown: Report Grounding and Grounded Report Generation, each with a medical image or document icon. Below, Use Cases are shown with icons depicting Education, Patient Explanation, and Report Visualization involving medical professionals and patients.

NEJM AI@ai· Jul 9

The future isn’t just clinician empowerment. It’s patient empowerment. In the latest episode of the NEJM AI Grand Rounds podcast, Dr. Karan Singal sees AI helping people navigate care. Hear more from Dr. Singal in the full episode: https://nejm.ai/ep43 #ArtificialIntelligence #AIinMedicine

NEJM AI@ai· Jul 8

Case Study by A. Jaech et al.: LLM-Assisted Reanalysis of Unsolved Rare Disease Genomes Increases Diagnostic Yield https://nejm.ai/4eMh8pu #ArtificialIntelligence #AIinMedicine

Image of a document titled "Family 0955 – Case Review for PATIENT_0955-01 (Hypophosphatemic Rickets)." The document outlines clinical background, an interpretation, and a conclusion. The following figure caption sits at the bottom: "Figure 1. Example Output from the OpenAI Tool from a Known Diagnosed Control Case."

NEJM AI@ai· Jul 6

Editorial by Samuel G. Finlayson, MD, PhD, and Heidi L. Rehm, PhD: When Genomic Reanalysis Leaves the Laboratory — Clinical Genetics in the Age of Consumer AI https://nejm.ai/4vVYCAY #ArtificialIntelligence #AIinMedicine

A quote from an editorial by Samuel G. Finlayson, M.D., Ph.D., and Heidi L. Rehm, Ph.D., published in NEJM AI. The quote reads as follows: "The appropriate posture is . . . neither to encourage patients to assume a duty they are not trained for, nor to obstruct an exercise of autonomy that is both legally grounded and technically feasible. Instead, the field must build the infrastructure that makes patient-generated findings safe, useful, and equitably available." The background has horizontal colored stripes and abstract patterns.

NEJM AI@ai· Jul 3

Sometimes the problem isn't the model. It's the environment around it. In the latest episode of the NEJM AI Grand Rounds podcast, Dr. Karan Singal explains why workflow design matters. Listen now: https://nejm.ai/ep43 #ArtificialIntelligence #AIinMedicine

NEJM AI@ai· Jul 2

ReXGroundingCT is the first publicly available manually annotated dataset to link free-text radiology findings with 3D segmentations in chest CT scans, enabling research in grounded radiology report generation. Learn more: https://nejm.ai/4giw8N6

Figure 1 illustrates the ReXGroundingCT Dataset. Panel A displays lung CT scan findings. Panel B highlights a bar chart of findings per category across the dataset, grouped by typical pattern (nonfocal vs. focal). Panel C details an example of anatomical chain-of-thought reasoning.

NEJM AI@ai· Jul 1

A new Case Study evaluates an LLM–assisted genomic reanalysis workflow that integrates clinician notes, Human Phenotype Ontology terms, and filtered variant data to generate diagnostic hypotheses for expert review. Learn more: https://nejm.ai/4eMh8pu

Diagram showing protein structure predictions related to the pathological mechanism of the Sphingosine-1-Phosphate Receptor 1 (S1PR1) c.850_882del variant in a vitiligo patient. Image includes a protein sequence diagram, ribbon model structures, and molecular surface models.

NEJM AI@ai· Jun 30

What if better models reduce — not increase — the value of traditional oversight? In the latest episode of the NEJM AI Grand Rounds podcast, Dr. Karan Singal discusses findings that challenge assumptions. Listen to the full episode: https://nejm.ai/ep43 #ArtificialIntelligence #AIinMedicine

NEJM AI@ai· Jun 29

A new editorial discusses whether clinical genetics is prepared for a world in which patients and families can generate rare disease analyses on their own. Full editorial: https://nejm.ai/4vVYCAY

The image is a quote from an editorial published in NEJM AI asking, "Is clinical genetics prepared for a world in which patients and families can generate analyses of this kind on their own?" Authors: Samuel G. Finlayson, M.D., Ph.D., and Heidi L. Rehm, Ph.D. The background is a gradient orange.

NEJM AI@ai· Jun 27

Human + AI isn't a fixed equation. In the latest episode of the NEJM AI Grand Rounds podcast, Dr. Karan Singal describes findings showing that oversight may depend on model capability. Learn more: https://nejm.ai/ep43 #ArtificialIntelligence #AIinMedicine